A neural network approach to multi-biomarker panel discovery by high-throughput plasma proteomics profiling of breast cancer

BMC Proceedings

Proceedings

Renee Drabier¹  Fan Zhang²  Mu Wang³  Jake Chen⁴ 

[1] Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA;Department of Academic and Institutional Resources and Technology, University of North Texas Health Science Center, Fort Worth, USA;Department of Forensic and Investigative Genetics, University of North Texas Health Science Center, Fort Worth, USA;Indiana Center for Systems Biology and Personalized Medicine, 46202, Indianapolis, IN, USA;Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, USA;School of Informatics, Indiana University, 46202, Indianapolis, IN, USA;Department of Computer and Information Science, School of Science, Purdue University, 46202, Indianapolis, IN, USA;Indiana Center for Systems Biology and Personalized Medicine, 46202, Indianapolis, IN, USA;Institute of Biopharmaceutical Informatics and Technology, Wenzhou Medical College, Wenzhou, Zhejiang, China;

关键词: Breast Cancer;    Prediction Performance;    Receiver Operating Characteristic;    Feed Forward Neural Network;    Data Split;   

DOI  :  10.1186/1753-6561-7-S7-S10

来源: Springer

【 摘 要 】

BackgroundIn the past several years, there has been increasing interest and enthusiasm in molecular biomarkers as tools for early detection of cancer. Liquid chromatography tandem mass spectrometry (LC/MS/MS) based plasma proteomics profiling technique is a promising technology platform to study candidate protein biomarkers for early detection of cancer. Factors such as inherent variability, protein detectability limitation, and peptide discovery biases among LC/MS/MS platforms have made the classification and prediction of proteomics profiles challenging. Developing proteomics data analysis methods to identify multi-protein biomarker panels for breast cancer diagnosis based on neural networks provides hope for improving both the sensitivity and the specificity of candidate cancer biomarkers for early detection.ResultsIn our previous method, we developed a Feed Forward Neural Network-based method to build the classifier for plasma samples of breast cancer and then applied the classifier to predict blind dataset of breast cancer. However, the optimal combination C* in our previous method was actually determined by applying the trained FFNN on the testing set with the combination. Therefore, in this paper, we applied a three way data split to the Feed Forward Neural Network for training, validation and testing based. We found that the prediction performance of the FFNN model based on the three way data split outperforms our previous method and the prediction performance is improved from (AUC = 0.8706, precision = 82.5%, accuracy = 82.5%, sensitivity = 82.5%, specificity = 82.5% for the testing set) to (AUC = 0.895, precision = 86.84%, accuracy = 85%, sensitivity = 82.5%, specificity = 87.5% for the testing set).ConclusionsFurther pathway analysis showed that the top three five-marker panels are associated with complement and coagulation cascades, signaling, activation, and hemostasis, which are consistent with previous findings. We believe the new approach is a better solution for multi-biomarker panel discovery and it can be applied to other clinical proteomics.

【 授权许可】

CC BY   
© Zhang et al.; licensee BioMed Central Ltd. 2013